Instructions to use rlhn/e5-base-remove-680K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rlhn/e5-base-remove-680K with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="rlhn/e5-base-remove-680K")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("rlhn/e5-base-remove-680K") model = AutoModel.from_pretrained("rlhn/e5-base-remove-680K") - Notebooks
- Google Colab
- Kaggle
Improve model card
#1
by nielsr HF Staff - opened
This PR improves the model card for the model presented in the paper Fixing Data That Hurts Performance: Cascading LLMs to Relabel Hard Negatives for Robust Information Retrieval.
This includes:
- adding relevant
pipeline_tagand metadata - linking to the related Github repository
- Removing the
[More Information Needed]prompts.